机译:全局图上的学习扩散:PDE导向的几何形状特征检测方法
Dalian Maritime Univ, Informat Sci & Technol, Dalian, Peoples R China;
Dalian Univ Technol, DUT RU Int Sch Informat & Software Engn, Dalian, Peoples R China;
Dalian Univ Technol, DUT RU Int Sch Informat & Software Engn, Dalian, Peoples R China;
SUNY Stony Brook, Comp Sci, Stony Brook, NY 11794 USA;
Dalian Univ Technol, Sch Math Sci, Dalian, Peoples R China;
Dalian Univ Technol, DUT RU Int Sch Informat & Software Engn, Dalian, Peoples R China|Guilin Univ Elect Technol, Inst Artificial Intelligence, Guilin, Peoples R China;
SUNY Stony Brook, Comp Sci, Stony Brook, NY 11794 USA;
Partial differential equations (PDEs); Global graph; Submodularity; Small-sample learning; Feature detection;
机译:在全局图中学习扩散:几何形状特征检测的PDE定向方法
机译:使用多个内核学习分类方法对象识别的几何不变颜色,形状和纹理特征
机译:从尺度不变特征变换关键点进行弱监督学习:一种结合快速特征分解,正则化和图扩散的方法
机译:使用组合的全局和局部形状模型对几何网格数据进行特征检测和跟踪以进行面部分析
机译:3D形状几何特征学习
机译:基于本地或全球不变的几何形状对卷积神经网络的学习转移
机译:从Scale Invariant Feature转换关键点的弱监督学习:一种方法,组合快速的eigendecompostion,正则化和图形扩散